Basis functions that can be used to approximate Gaussian processes with shift invariant covariance functions (e.g. square exponential) when used with linear models [1], [2], [3].

Non-Gaussian likelihoods with Bayesian generalized linear models (GLMs). We infer all of the parameters in the GLMs using stochastic variational inference [4], and we approximate the posterior over the weights with a mixture of Gaussians, like [5].

Now there is a random search phase before optimization of all hyperparameters in the regression algorithms. This improves the performance of revrand since local optima are more easily avoided with this improved initialisation

Other available revisons

Now there is a random search phase before optimization of all hyperparameters in the regression algorithms. This improves the performance of revrand since local optima are more easily avoided with this improved initialisation

Now there is a random search phase before optimization of all hyperparameters in the regression algorithms. This improves the performance of revrand since local optima are more easily avoided with this improved initialisation.